Introduction to Transparent Bayesian Data Analysis (chi2023-course)
Abhraneel Sarma and Fumeng Yang
03 April, 2023
Introduction
In this document, we will outline the Bayesian analogs of the statistical analyses described in lecture 1 (Github code).
Note on MathJax
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Load packages
library(dplyr)
library(tibble)
library(purrr)
library(patchwork)
library(tidyr)
library(forcats)
library(gtools)
library(broom)
library(broom.mixed)
library(modelr)
library(brms)
library(tidybayes)
library(ggdist)
library(bayesplot)
library(ggplot2)
library(knitr)
BRM_BACKEND <- ifelse(require("cmdstanr"), 'cmdstanr', 'rstan')Dataset
The following dataset is from experiment 2 of “How Relevant are Incidental Power Poses for HCI?” (Jansen & Hornbæk, 2018). Study participants were asked to either make an expansive posture or a constrictive posture before performing a task. The experiment investigated whether posture could potentially have an effect on risk taking behavior.
First, we load the data.
pose_df = readr::read_csv("data/poses_data.csv", show_col_types = FALSE) %>%
mutate(condition) %>%
group_by(participant)The data has been aggregated for each participant: -
condition = expansive indicates expansive posture, and
condition = constrictive indicates constrictive posture -
The dependent variable is change which indicates the
percentage change in risk-taking behavior. Thus, it is a continuous
variable.
For the purposes of this demo, we are only concerned with these two variables. We can ignore the other variables for now.
head(pose_df %>% select(participant, condition, change))| participant | condition | change |
|---|---|---|
| 1 | expansive | 3.362832 |
| 2 | constrictive | 29.147982 |
| 3 | expansive | 25.409836 |
| 4 | constrictive | 54.069767 |
| 5 | expansive | -36.644592 |
| 6 | constrictive | 29.756098 |
Intuition of Bayesian Statistics
The Bayesian t-test (BEST) assumes that the data in the two conditions arises from two separate t-distributions. In the following section, we will describe the process for one of the conditions in the experiment.
We will use the \(Normal(\mu = 20, \sigma = 20)\) as the prior distribution.
First, we define some functions for manual calculation of the posterior normal distribution:
sigma_post = function(sigma_prior, sigma, n = 1) {
sqrt(1 / (1 / (sigma_prior^2) + n / (sigma^2)))
}
mu_post = function(mu_prior, sigma_prior, mu, sigma, n = 1) {
tau = sigma_post(sigma_prior, sigma, n)
(tau^2 / sigma_prior^2)*mu_prior + (n * tau^2 / sigma^2)*mu
}d.p2 = tibble(
group = c("prior", "expansive", "constrictive", "posterior"),
mu = c(20, 32.82, 31.61, mu_post(20, 20, 32.82, 7.52)),
sd = c(20, 7.52, 7.06, sigma_post(20, 7.52))
) %>%
mutate(
cutoff_group = list(c(1:7)),
cutoff = list(c(0, 15, 25, 30, 32.82, 40, 100))
) %>%
unnest(c(cutoff_group, cutoff)) %>%
mutate(
cutoff = if_else(group == 'prior', 100, cutoff),
x = map(cutoff, ~ seq(from = -40, ., by = 0.1)),
y = pmap(list(x, mu, sd), ~ dnorm(..1, ..2, ..3))
)In the plot below, we show the raw data distribution for the two conditions:
p1 = pose_df %>%
ggplot() +
geom_point(aes(x = change, y = condition, colour = condition),
position = position_jitter(height = 0.1), alpha = 0.7) +
scale_color_theme() +
labs(y = "Condition") +
theme(
legend.position = "none",
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank()
) +
scale_x_continuous(limits = c(-150, 250), breaks = seq(-150, 250, by = 50))
p2.blank = tibble(y = c("expansive", "constrictive"), x = 0) %>%
ggplot(aes(x, y)) +
scale_color_theme() +
theme_density
cowplot::plot_grid(p2.blank, p1, nrow = 2)
First, we define a function to help us plot different cutoff groups.
plot_preliminary <- function(data, group_num, group_name){
data %>%
filter(cutoff_group == group_num & group %in% group_name) %>%
unnest(c(x, y)) %>%
ggplot(aes(x, y)) +
#geom_line(aes(color = group), size = 1) +
# density
geom_area(aes(fill = group, color = group), position = "identity", linewidth = 1, alpha = 0.3) +
scale_x_continuous(limits = c(-40, 100)) +
scale_y_continuous(limits = c(0, 0.1)) +
scale_color_theme() +
theme_density
}cowplot::plot_grid(
plot_preliminary(d.p2, 0, NULL),
p1, nrow = 2)
Next, we plot the prior density:
cowplot::plot_grid(
plot_preliminary(d.p2, 1, 'prior'),
p1, nrow = 2)
Then we describe step by step, how the likelihood is computed:
cowplot::plot_grid(
plot_preliminary(d.p2, 1, 'expansive'),
p1, nrow = 2)
cowplot::plot_grid(
plot_preliminary(d.p2, 2, 'expansive'),
p1, nrow = 2)
cowplot::plot_grid(
plot_preliminary(d.p2, 3, 'expansive'),
p1, nrow = 2)
cowplot::plot_grid(
plot_preliminary(d.p2, 5, 'expansive'),
p1, nrow = 2)
cowplot::plot_grid(
plot_preliminary(d.p2, 6, 'expansive'),
p1, nrow = 2)
cowplot::plot_grid(
plot_preliminary(d.p2, 7, 'expansive'),
p1, nrow = 2)
We want to compute the posterior, which is the product of the prior and likelihood:
cowplot::plot_grid(
plot_preliminary(d.p2, 7, c('prior', 'expansive', 'posterior')),
p1, nrow = 2)
Of course, if you have gganimate and magick
working on your computer, you can try the following code to get a gif!
Here, we are not going to show the code.
Fumeng: the content below needs better documentation….
Model 1: Equal standard deviations
Step 1: model specification
Fumeng: I hope to keep this example simple. Is there a simple way to justify the choice of \(\nu\)?
Here we use a mathematical expression for the model above. Fumeng: need explanations here…
\[ \begin{align} y_{i} &\sim \mathrm{Student\_t}(\mu, \sigma_{0}, \nu_{0}) \\ \mu &= \beta_{0} + \beta_{1} * x_i \\ \sigma_{0} &\sim \mathrm{HalfNormal}(0, 10) \\ \beta_{0} &\sim \mathrm{?} \\ \beta_{1} &\sim \mathrm{Normal}(0,2) \\ \nu_{0} &\sim \mathrm{?} \\ i & \in \{\mathrm{expansive}, \mathrm{constrictive}\} \end{align} \]
We translate this thought into brms formula using the
function bf.
model.1.formula <- bf(
# we think change is affected by different conditions.
change ~ condition,
# to tell brms which response distribution to use
family = student()
)Now we can use get_prior() to inspect the available
priors and formula. As what we learned, we have priors for
tibble(get_prior(model.1.formula, pose_df))| prior | class | coef | group | resp | dpar | nlpar | lb | ub | source |
|---|---|---|---|---|---|---|---|---|---|
| b | default | ||||||||
| b | conditionexpansive | default | |||||||
| student_t(3, 22.6, 38.8) | Intercept | default | |||||||
| gamma(2, 0.1) | nu | 1 | default | ||||||
| student_t(3, 0, 38.8) | sigma | 0 | default |
prior checks
Look at the default priors
cowplot::plot_grid(
tibble(x = qstudent_t(ppoints(n = 500), df = 3, mu = 22.6, sigma = 38.8)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('student_t(3, 22.6, 38.8) for Intercept') +
coord_cartesian(xlim = c(-300, 300),expand = c(0)),
tibble(x = qgamma(ppoints(n = 500), shape = 2, rate = .1)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('Gamma(2, 0.1) for nu') +
coord_cartesian(xlim = c(1, 100), expand = 0),
tibble(x = qstudent_t(ppoints(n = 500), df = 3, mu = 0, sigma = 38.8)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('student_t(3, 0, 38.8) for sigma') +
coord_cartesian(xlim = c(0, 400),expand = c(0)),
ncol = 3)
prior check for default priors
model.1.checks_default <- brm(
model.1.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 3), class = 'b')
),
sample_prior = 'only',
#backend = BRM_BACKEND,
file = 'rds/model.1.checks_default.rds',
file_refit = 'on_change'
)plot
model.1.defaultpriorsamples <-
model.1.checks_default %>%
epred_draws(tibble(condition = c('expansive', 'constrictive')))head(model.1.defaultpriorsamples)| condition | .row | .chain | .iteration | .draw | .epred |
|---|---|---|---|---|---|
| expansive | 1 | NA | NA | 1 | 4.919106 |
| expansive | 1 | NA | NA | 2 | 25.242105 |
| expansive | 1 | NA | NA | 3 | 15.514846 |
| expansive | 1 | NA | NA | 4 | 61.385267 |
| expansive | 1 | NA | NA | 5 | 98.523079 |
| expansive | 1 | NA | NA | 6 | -60.393775 |
model.1.defaultpriorsamples %>%
ggplot(aes(x = .epred, group = condition)) +
geom_density(alpha = .5, color = NA, adjust = 2, fill = theme_yellow) +
theme_density_x +
ggtitle('Checks default priors of model.1')
Our priors
cowplot::plot_grid(
tibble(x = qnorm(ppoints(n = 1000), mean = 0, sd = 2)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('Normal(0, 2) for b & Intercept') +
coord_cartesian(xlim = c(-8, 8), expand = 0),
tibble(x = qnorm(ppoints(n = 1000), mean = 0, sd = 10)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('HalfNormal(0, 10) for sigma') +
coord_cartesian(xlim = c(0, 50), expand = c(0)),
ncol = 2)
model.1.checks <- brm(
model.1.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 2), class = 'b'),
prior(normal(0, 10), class = 'sigma', lb = 0)
),
sample_prior = 'only',
#backend = BRM_BACKEND,
file = 'rds/model.1.checks.rds',
file_refit = 'on_change'
)plot
model.1.priorsamples <-
model.1.checks %>%
epred_draws(tibble(condition = c('expansive', 'constrictive')))Compare two sets of priors
cowplot::plot_grid(
model.1.defaultpriorsamples %>%
ggplot(aes(x = .epred, group = condition)) +
geom_density(alpha = .5, color = NA, adjust = 2, fill = theme_yellow) +
theme_density_x +
#coord_cartesian(xlim = c(-800, 800)) +
ggtitle('Checks default priors of model.1')
,
model.1.priorsamples %>%
ggplot(aes(x = .epred, group = condition)) +
geom_density(alpha = .5, color = NA, adjust = 2, fill = theme_yellow) +
theme_density_x +
#coord_cartesian(xlim = c(-800, 800)) +
ggtitle('Checks our priors of model.1')
,
ncol = 1)
Step 2: model fitting
model.1 <- brm(
model.1.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 2), class = 'b'),
prior(normal(0, 10), class = 'sigma', lb = 0)
),
backend = BRM_BACKEND,
file = 'rds/model.1.rds'
)Step 3: check posteriors
aspect 1: mcmc traces
color_scheme_set("teal")
mcmc_trace(model.1, facet_args = list(ncol = 4))
aspect 2: model metrics
Rhat, ESS, etc here
summary(model.1)## Family: student
## Links: mu = identity; sigma = identity; nu = identity
## Formula: change ~ condition
## Data: pose_df (Number of observations: 80)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 22.84 4.49 14.50 31.77 1.00 2686 2092
## conditionexpansive -0.26 1.97 -4.10 3.62 1.00 2726 2987
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 28.35 4.13 20.76 36.92 1.00 2180 2520
## nu 3.68 2.35 1.57 9.04 1.00 2172 2158
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).aspect 3: visually
Fumeng: do we need this?
model.1.predictions <-
model.1 %>%
predicted_draws(tibble(condition = c('expansive', 'constrictive')))head(model.1.predictions)| condition | .row | .chain | .iteration | .draw | .prediction |
|---|---|---|---|---|---|
| expansive | 1 | NA | NA | 1 | -0.6345021 |
| expansive | 1 | NA | NA | 2 | 42.4771348 |
| expansive | 1 | NA | NA | 3 | -8.2274815 |
| expansive | 1 | NA | NA | 4 | -10.1393149 |
| expansive | 1 | NA | NA | 5 | 18.9937838 |
| expansive | 1 | NA | NA | 6 | -35.5627088 |
plot_predictions <- function(model, df = NULL, title = ''){
if(is.null(df))
df = tibble(condition = c('expansive', 'constrictive'),
participant = c(-1, -1))
model %>%
predicted_draws(df,
seed = 1234,
ndraws = NULL,
allow_new_levels = TRUE,
sample_new_levels = 'uncertainty') %>%
ggplot(aes(x = .prediction, fill = condition)) +
geom_density(alpha = .5, size = 1, adjust = 2, color = NA) +
scale_color_theme() +
theme_density_x +
scale_y_continuous(breaks = 0, labels = 'constrictive') +
scale_x_continuous(breaks = seq(-150, 250, by = 50)) +
coord_cartesian(xlim = c(-150, 250)) +
ggtitle(paste0('Posterior predictions of ', deparse(substitute(model))))
}cowplot::plot_grid(plot_predictions(model.1),
p1 +
scale_x_continuous(breaks = seq(-150, 250, by = 100)) +
coord_cartesian(xlim = c(-150, 250)),
nrow = 2)
model.1.posteriors <-
model.1 %>%
epred_draws(tibble(condition = c('expansive', 'constrictive')))plot_posteriors <- function(model, df = NULL, title = ''){
if(is.null(df))
df = tibble(condition = c('expansive', 'constrictive'))
model %>%
epred_draws(df,
# ignoring random effects if there is any
#seed = 1234,
ndraws = NULL,
re_formula = NA) %>%
ggplot(aes(x = .epred, fill = condition)) +
geom_density(alpha = .5, size = 1, adjust = 2, color = NA) +
scale_color_theme() +
theme_density_x +
scale_y_continuous(breaks = 0, labels = 'constrictive') +
scale_x_continuous(limits = c(-150, 250), breaks = seq(-150, 250, by = 50)) +
ggtitle(paste0('Posterior means of ', deparse(substitute(model))))
}cowplot::plot_grid(plot_posteriors(model.1), p1, nrow = 2)
Model 2: the BEST test model
Step 1: model specification
This model is the BEST test model as described by Kruschke in the paper Bayesian estimation supersedes the t-test. In this model, \(\beta\) indicates the mean difference in the outcome variable between the two groups (in this case, the percent change in the BART scores). We fit different priors on \(\beta\) and set different weights on these priors to obtain our posterior estimate.
\[ \begin{align} y_{i} &\sim \mathrm{T}(\nu, \mu, \sigma) \\ \mu &= \beta_{0} + \beta_{1} * x_i \\ \sigma &= \sigma_{a} + \sigma_{b}*x_i \\ \beta_{1} &\sim \mathrm{Normal}(\mu_{0}, \sigma_{0}) \\ \sigma_a, \sigma_b &\sim \mathrm{Cauchy}(0, 2) \\ \nu &\sim \mathrm{exp}(1/30)\\ i & \in \{\mathrm{expansive}, \mathrm{constrictive}\} \end{align} \]
model.2.formula <- bf(# we think change is affected by different conditions.
change ~ condition,
sigma ~ condition,
# to tell brms which response distribution to use
family = student())tibble(get_prior(model.2.formula, pose_df))| prior | class | coef | group | resp | dpar | nlpar | lb | ub | source |
|---|---|---|---|---|---|---|---|---|---|
| b | default | ||||||||
| b | conditionexpansive | default | |||||||
| student_t(3, 22.6, 38.8) | Intercept | default | |||||||
| gamma(2, 0.1) | nu | 1 | default | ||||||
| b | sigma | default | |||||||
| b | conditionexpansive | sigma | default | ||||||
| student_t(3, 0, 2.5) | Intercept | sigma | default |
prior check
Fumeng: not sure qcauchy has the correct paramaterization
cowplot::plot_grid(
tibble(x = qnorm(ppoints(n = 1000), mean = 0, sd = 2)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('Normal(0 ,2) for Intercept') +
coord_cartesian(expand = c(0)),
tibble(x = qexp(ppoints(n = 1000), rate = 0.0333)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('exponential(0.0333) for nu') +
coord_cartesian(expand = 0),
tibble(x = qcauchy(ppoints(n = 1000), location = 0, scale = 2)) %>%
ggplot() +
geom_density(aes(x = x), fill = theme_yellow, color = NA) +
ggtitle('cauchy(0, 2) for sigma') +
coord_cartesian(expand = c(0)),
ncol = 3)
model.2.priorchecks <- brm(
model.2.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 2), class = 'b'),
prior(cauchy(0, 2), class = 'b', dpar = 'sigma'),
prior(exponential(0.0333), class = 'nu')
),
sample_prior = 'only',
#backend = BRM_BACKEND,
file = 'rds/model.2.priorchecks.rds',
file_refit = 'on_change'
)model.2.priorchecks %>%
epred_draws(tibble(condition = c('expansive', 'constrictive'))) %>%
ggplot(aes(x = .epred, group = condition)) +
geom_density(alpha = .5, color = NA, adjust = 2, fill = theme_yellow) +
theme_density_x +
ggtitle('Checks priors of model.2')
Step 2: model fiting
model.2 <- brm(
model.2.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 2), class = 'b'),
prior(cauchy(0, 2), class = 'b', dpar = 'sigma'),
prior(exponential(0.0333), class = 'nu')
),
backend = BRM_BACKEND,
file = 'rds/model.2.rds',
file_refit = 'on_change'
)Step 3: posterior checks
summary(model.2)## Family: student
## Links: mu = identity; sigma = log; nu = identity
## Formula: change ~ condition
## sigma ~ condition
## Data: pose_df (Number of observations: 80)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 24.56 4.99 15.10 34.76 1.00 2787
## sigma_Intercept 3.41 0.20 3.00 3.79 1.00 2307
## conditionexpansive -0.11 1.99 -4.11 3.76 1.00 3769
## sigma_conditionexpansive 0.15 0.21 -0.27 0.57 1.00 3783
## Tail_ESS
## Intercept 2788
## sigma_Intercept 2293
## conditionexpansive 2642
## sigma_conditionexpansive 2778
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu 6.00 7.42 1.72 23.39 1.00 1910 1876
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).wrap_plots(plot_predictions(model.2) + scale_x_continuous(limits = c(-200,250), expand = c(0,0), breaks = seq(-150, 250, by = 50))
,
p1 + scale_x_continuous(limits = c(-200,250), expand = c(0,0), breaks = seq(-150, 250, by = 50)) ,
nrow = 2)
# draws.model.3 =
wrap_plots(
plot_posteriors(model.2),
p1,
nrow = 2)
model.2.posteriors <- model.2 %>%
epred_draws(tibble(condition = c('expansive', 'constrictive')),
#seed = 1234,
ndraws = NULL,
re_formula = NA) Model 3: Poisson / Negative-Binomial Regression model
Step 1: model specification
\[ \begin{align} y_{i} &\sim \mathrm{Neg-Binomial}(\mu, \phi) \\ \mu &= \beta_{i,j} + \beta_{1} * x_i \\ \beta_{1} &\sim \mathrm{Normal}(0, 2) \\ i & \in \{\mathrm{expansive}, \mathrm{constrictive}\}\\ j & \in \{1, ..., \mathrm{N}\} \end{align} \]
pose_raw_df = read.csv("data/posture_data-raw.csv") %>%
mutate(participant = factor(participant)) %>%
rename(trial = trial.number)
head(pose_raw_df)| participant | condition | total.money | trial | trial.money | exploded | pumps | life |
|---|---|---|---|---|---|---|---|
| 1 | expansive | 910 | 0 | 62 | 0 | 62 | 72 |
| 1 | expansive | 910 | 1 | 0 | 1 | 27 | 27 |
| 1 | expansive | 910 | 2 | 47 | 0 | 47 | 98 |
| 1 | expansive | 910 | 3 | 0 | 1 | 86 | 86 |
| 1 | expansive | 910 | 4 | 60 | 0 | 60 | 104 |
| 1 | expansive | 910 | 5 | 0 | 1 | 26 | 26 |
model.3= brm(pumps ~ 1 + condition + trial + (trial | participant),
data = pose_raw_df, family = negbinomial,
prior = c(prior(normal(0, 10), class = Intercept),
prior(normal(0, 1), class = b),
prior(gamma(0.01, 0.01), class = shape)
), # this is the brms default
file = 'rds/model.3.rds',
file_refit = 'on_change' ,
iter = 4000, warmup = 1000, cores = 4, chains = 4)summary(model.3)## Family: negbinomial
## Links: mu = log; shape = identity
## Formula: pumps ~ 1 + condition + trial + (trial | participant)
## Data: pose_raw_df (Number of observations: 2400)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Group-Level Effects:
## ~participant (Number of levels: 80)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sd(Intercept) 0.37 0.04 0.31 0.45 1.00 3834
## sd(trial) 0.01 0.00 0.00 0.01 1.00 3766
## cor(Intercept,trial) -0.62 0.12 -0.82 -0.35 1.00 9335
## Tail_ESS
## sd(Intercept) 6032
## sd(trial) 4648
## cor(Intercept,trial) 8296
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 3.52 0.06 3.41 3.63 1.00 2896 5345
## conditionexpansive -0.04 0.07 -0.17 0.10 1.00 3288 5424
## trial 0.01 0.00 0.00 0.01 1.00 6928 8176
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 7.66 0.28 7.14 8.22 1.00 17558 8043
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).Step 2: model interpretation
The results of this model are on the log-odds scale. What do the coefficients mean? The simplest way is to simply transform the data into a more interpretable scale and visualise the results:
# draws.model.3 =
wrap_plots(
plot_predictions(model.3, df = crossing(condition = c('expansive', 'constrictive'),
trial = 29,
participant = 0)),
p1,
nrow = 2)
# draws.model.3 =
wrap_plots(
plot_posteriors(model.3, df = tibble(condition = c('expansive', 'constrictive'),
trial = 29,
participant = 0)),
p1,
nrow = 2)
model.3.posteriors <- model.3 %>%
epred_draws(tibble(condition = c('expansive', 'constrictive'),
trial = 29,
participant = 0),
allow_new_levels = TRUE) %>%
mutate(model = 'model 3') From this, we can see that there does not appear to be a difference between the two conditions.
Model 4: the SAYA model
SAYA = SArma + YAng
Fumeng: do we want to get into random intercepts?
Step 1: model specification
\[ \begin{align} y_{i} &\sim \mathrm{T}(\nu, \mu, \sigma) \\ \mu &= \beta_{i,j} + \beta_{1} * x_i \\ \sigma &= \sigma_{a} + \sigma_{b}*x_i \\ \beta_{1} &\sim \mathrm{Normal}(0, 2) \\ \sigma_a, \sigma_b &\sim \mathrm{HalfNormal}(0, 10) \\ \nu &\sim \mathrm{exp}(1/30)\\ i & \in \{\mathrm{expansive}, \mathrm{constrictive}\}\\ j & \in \{1, ..., \mathrm{N}\} \end{align} \]
model.4.formula <- bf(# we think change is affected by different conditions.
change ~ condition + (1|participant),
sigma ~ condition,
# to tell brms which response distribution to use
family = student())tibble(get_prior(model.4.formula, pose_df))| prior | class | coef | group | resp | dpar | nlpar | lb | ub | source |
|---|---|---|---|---|---|---|---|---|---|
| b | default | ||||||||
| b | conditionexpansive | default | |||||||
| student_t(3, 22.6, 38.8) | Intercept | default | |||||||
| gamma(2, 0.1) | nu | 1 | default | ||||||
| student_t(3, 0, 38.8) | sd | 0 | default | ||||||
| sd | participant | default | |||||||
| sd | Intercept | participant | default | ||||||
| b | sigma | default | |||||||
| b | conditionexpansive | sigma | default | ||||||
| student_t(3, 0, 2.5) | Intercept | sigma | default |
Step 2: model fiting
model.4 <- brm(
model.4.formula,
data = pose_df,
family = student_t(),
prior = c(
prior(normal(0, 2), class = 'b'),
prior(normal(0, 2), class = 'b', dpar = 'sigma'),
prior(normal(0, 2), class = 'sd', group = 'participant', lb = 0),
prior(exponential(0.0333), class = 'nu')
),
#backend = BRM_BACKEND,
file = 'rds/model.4.rds',
file_refit = 'on_change'
)Step 3: posterior checks
summary(model.4)## Family: student
## Links: mu = identity; sigma = log; nu = identity
## Formula: change ~ condition + (1 | participant)
## sigma ~ condition
## Data: pose_df (Number of observations: 80)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~participant (Number of levels: 80)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.58 1.20 0.07 4.40 1.00 3406 2185
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 24.38 5.00 15.24 34.67 1.00 6113
## sigma_Intercept 3.40 0.20 2.98 3.79 1.00 5980
## conditionexpansive -0.10 1.88 -3.87 3.69 1.01 8773
## sigma_conditionexpansive 0.15 0.22 -0.28 0.59 1.00 8215
## Tail_ESS
## Intercept 3424
## sigma_Intercept 3032
## conditionexpansive 2856
## sigma_conditionexpansive 2495
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu 5.78 6.79 1.68 22.11 1.00 4474 2774
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).cowplot::plot_grid(plot_predictions(model.4), p1, nrow = 2)
cowplot::plot_grid(plot_posteriors(model.4), p1, nrow = 2)
model.4.posteriors <-
model.4 %>%
epred_draws(tibble(condition = c('expansive', 'constrictive')),
re_formula = NA)wrap_plots(
model.4.posteriors %>%
mutate(model = 'model 4') %>%
rbind(
model.3.posteriors %>%
mutate(model = 'model 3')) %>%
rbind(
model.2.posteriors %>%
mutate(model = 'model 2')) %>%
rbind(
model.1.posteriors %>%
mutate(model = 'model 1')) %>%
ggplot() +
geom_density(aes(x = .epred, fill = condition), adjust = 1.5, color = NA, alpha = .5) +
facet_grid(model ~ .) +
scale_x_continuous(limits = c(-150, 250), breaks = seq(-150, 250, by = 50)) +
scale_color_theme() +
theme_density_x +
ggtitle('Compare the means of all four models'),
p1, nrow = 2, heights = c(4,1.5))
Reporting
Let’s use model 4
model
summary(model.4)## Family: student
## Links: mu = identity; sigma = log; nu = identity
## Formula: change ~ condition + (1 | participant)
## sigma ~ condition
## Data: pose_df (Number of observations: 80)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~participant (Number of levels: 80)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.58 1.20 0.07 4.40 1.00 3406 2185
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 24.38 5.00 15.24 34.67 1.00 6113
## sigma_Intercept 3.40 0.20 2.98 3.79 1.00 5980
## conditionexpansive -0.10 1.88 -3.87 3.69 1.01 8773
## sigma_conditionexpansive 0.15 0.22 -0.28 0.59 1.00 8215
## Tail_ESS
## Intercept 3424
## sigma_Intercept 3032
## conditionexpansive 2856
## sigma_conditionexpansive 2495
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu 5.78 6.79 1.68 22.11 1.00 4474 2774
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).CI
model.4.posteriors.CI <-
model.4.posteriors %>%
group_by(condition) %>%
median_qi(.epred, width = .95)model.4.posteriors %>%
ggplot() +
geom_density(aes(x = .epred, fill = condition), alpha = .5, adjust = 2, color = NA) +
geom_point(model.4.posteriors.CI,
mapping = aes(x = .epred, y = 0), size = 3) +
geom_errorbarh(model.4.posteriors.CI,
mapping = aes(x = .epred, xmin = .epred.lower, xmax = .epred.upper, y = 0), height = 0, linewidth = 1.5) +
facet_wrap(condition ~ ., ncol = 1) +
scale_x_continuous(limits = c(0, 50)) +
scale_y_continuous(expand = c(.02,.02)) +
scale_color_theme() +
theme_density_x 
subtraction
model.4.posteriors_diff <-
model.4.posteriors %>%
compare_levels(variable = .epred, by = condition) %>%
select(-.chain, -.iteration) %>%
ungroup()head(model.4.posteriors_diff)| .draw | condition | .epred |
|---|---|---|
| 1 | expansive - constrictive | -1.8591500 |
| 2 | expansive - constrictive | 1.6581828 |
| 3 | expansive - constrictive | -1.0066992 |
| 4 | expansive - constrictive | 5.0253065 |
| 5 | expansive - constrictive | 1.0634401 |
| 6 | expansive - constrictive | 0.0398311 |
model.4.posteriors_diff.CI <-
model.4.posteriors_diff %>%
median_qi(.epred)
model.4.posteriors_diff.CI| .epred | .lower | .upper | .width | .point | .interval |
|---|---|---|---|---|---|
| -0.0867433 | -3.866209 | 3.691605 | 0.95 | median | qi |
model.4.posteriors_diff %>%
ggplot() +
geom_density(aes(x = .epred), alpha = .5, fill = theme_blue, color = NA, adjust = 2) +
geom_point(model.4.posteriors_diff.CI,
mapping = aes(x = .epred, y = 0), size = 3) +
geom_errorbarh(model.4.posteriors_diff.CI,
mapping = aes(x = .epred, xmin = .lower, xmax = .upper, y = 0), height = 0, linewidth = 1.5) +
#scale_x_continuous(limits = c(-50, 50)) +
xlab('expansive - constrictive') +
ggtitle('Mean difference in expansive and constrictive') +
geom_vline(xintercept = 0, linetype = 2) +
scale_y_continuous(expand = c(.02,.02)) +
scale_x_continuous(limits = c(-10, 10), breaks = seq(-10, 10, by = 5)) +
scale_color_theme() +
theme_density_x 
Session info
sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] magick_2.7.3 gganimate_1.0.8 cmdstanr_0.5.2
## [4] knitr_1.39 ggplot2_3.4.0 bayesplot_1.9.0
## [7] ggdist_3.1.1 tidybayes_3.0.2 brms_2.17.0
## [10] Rcpp_1.0.8.3 modelr_0.1.8 broom.mixed_0.2.9.4
## [13] broom_1.0.0 gtools_3.9.2.1 forcats_0.5.1
## [16] tidyr_1.2.0 patchwork_1.1.2 purrr_0.3.4
## [19] tibble_3.1.7 dplyr_1.0.9
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-3 ellipsis_0.3.2 ggridges_0.5.3
## [4] markdown_1.1 base64enc_0.1-3 rstudioapi_0.13
## [7] listenv_0.8.0 furrr_0.3.1 farver_2.1.1
## [10] rstan_2.21.5 bit64_4.0.5 svUnit_1.0.6
## [13] DT_0.23 fansi_1.0.3 mvtnorm_1.1-3
## [16] diffobj_0.3.5 bridgesampling_1.1-2 codetools_0.2-18
## [19] splines_4.2.2 shinythemes_1.2.0 jsonlite_1.8.0
## [22] shiny_1.7.1 readr_2.1.2 compiler_4.2.2
## [25] backports_1.4.1 assertthat_0.2.1 Matrix_1.5-1
## [28] fastmap_1.1.0 cli_3.4.1 tweenr_1.0.2
## [31] later_1.3.0 htmltools_0.5.2 prettyunits_1.1.1
## [34] tools_4.2.2 igraph_1.3.1 coda_0.19-4
## [37] gtable_0.3.0 glue_1.6.2 reshape2_1.4.4
## [40] posterior_1.2.1 jquerylib_0.1.4 vctrs_0.5.1
## [43] nlme_3.1-160 crosstalk_1.2.0 tensorA_0.36.2
## [46] xfun_0.31 stringr_1.4.0 globals_0.15.0
## [49] ps_1.7.0 mime_0.12 miniUI_0.1.1.1
## [52] lifecycle_1.0.3 future_1.26.1 zoo_1.8-10
## [55] scales_1.2.0 vroom_1.5.7 colourpicker_1.1.1
## [58] hms_1.1.1 promises_1.2.0.1 Brobdingnag_1.2-7
## [61] parallel_4.2.2 inline_0.3.19 shinystan_2.6.0
## [64] yaml_2.3.5 gridExtra_2.3 loo_2.5.1
## [67] StanHeaders_2.21.0-7 sass_0.4.1 stringi_1.7.6
## [70] highr_0.9 dygraphs_1.1.1.6 gifski_1.6.6-1
## [73] checkmate_2.1.0 pkgbuild_1.3.1 rlang_1.0.6
## [76] pkgconfig_2.0.3 matrixStats_0.62.0 distributional_0.3.0
## [79] evaluate_0.15 lattice_0.20-45 labeling_0.4.2
## [82] rstantools_2.2.0 htmlwidgets_1.5.4 cowplot_1.1.1
## [85] bit_4.0.4 tidyselect_1.1.2 processx_3.5.3
## [88] parallelly_1.31.1 plyr_1.8.7 magrittr_2.0.3
## [91] R6_2.5.1 generics_0.1.3 DBI_1.1.2
## [94] pillar_1.7.0 withr_2.5.0 xts_0.12.1
## [97] abind_1.4-5 crayon_1.5.1 arrayhelpers_1.1-0
## [100] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.14
## [103] progress_1.2.2 grid_4.2.2 callr_3.7.0
## [106] threejs_0.3.3 digest_0.6.29 xtable_1.8-4
## [109] httpuv_1.6.5 RcppParallel_5.1.5 stats4_4.2.2
## [112] munsell_0.5.0 bslib_0.3.1 shinyjs_2.1.0